Modified LAB Algorithm with Clustering-based Search Space Reduction Method for solving Engineering Design Problems

• URL: http://arxiv.org/abs/2310.03055v1
• Date: Wed, 4 Oct 2023 12:35:13 GMT
• Title: Modified LAB Algorithm with Clustering-based Search Space Reduction Method for solving Engineering Design Problems
• Authors: Ruturaj Reddy, Utkarsh Gupta, Ishaan Kale, Apoorva Shastri, Anand J Kulkarni
• Abstract summary: A modified LAB algorithm is introduced in this paper. The proposed algorithm incorporates the roulette wheel approach and a reduction factor introducing inter-group competition. The algorithm exhibited improved and superior robustness as well as search space exploration capabilities.
• Score: 0.7789406630452325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A modified LAB algorithm is introduced in this paper. It builds upon the original LAB algorithm (Reddy et al. 2023), which is a socio-inspired algorithm that models competitive and learning behaviours within a group, establishing hierarchical roles. The proposed algorithm incorporates the roulette wheel approach and a reduction factor introducing inter-group competition and iteratively narrowing down the sample space. The algorithm is validated by solving the benchmark test problems from CEC 2005 and CEC 2017. The solutions are validated using standard statistical tests such as two-sided and pairwise signed rank Wilcoxon test and Friedman rank test. The algorithm exhibited improved and superior robustness as well as search space exploration capabilities. Furthermore, a Clustering-Based Search Space Reduction (C-SSR) method is proposed, making the algorithm capable to solve constrained problems. The C-SSR method enables the algorithm to identify clusters of feasible regions, satisfying the constraints and contributing to achieve the optimal solution. This method demonstrates its effectiveness as a potential alternative to traditional constraint handling techniques. The results obtained using the Modified LAB algorithm are then compared with those achieved by other recent metaheuristic algorithms.

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